An Echo State Network for fuel cell lifetime prediction under a dynamic micro-cogeneration load profile

Rania Mezzi, Nadia Yousfi-Steiner, Marie Cécile Péra, Daniel Hissel and Laurent Larger

Applied Energy, 2021, vol. 283, issue C, No S0306261920316834

Abstract: Improving Proton Exchange Membrane Fuel Cell durability is a key that paves the way to its large scale industrial deployment. During the last five years, the prognostics discipline emerged as an interesting field for Proton Exchange Membrane Fuel Cell state of health prediction and lifetime estimation. The information provided by the prognostic module is crucial for optimizing the control strategy to extend the fuel cell lifetime. In this paper, an approach based on Echo State Network for fuel cell prognostics under a variable load is developed. The novelty of this paper is to perform prognostics under a variable load profile without prior knowledge of this latter. Two solutions are developed in this work. The first one consists of evaluating the remaining useful lifetime under a repeated load cycle. The second one is based on using Markov chains to generate estimations of the future load profile, allowing thus to overcome the need of real future load profile prior knowledge. Both proposed solutions give accurate prediction results of proton exchange membrane fuel cell remaining useful lifetime, with low uncertainties.

Keywords: PEMFC; Durability; Prognostics; ESN; Markov chains; Variable load; Artificial intelligence (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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DOI: 10.1016/j.apenergy.2020.116297

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